CN110070235B - Flexible scheduling method for multiple mobile robots - Google Patents
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Abstract
The invention discloses a flexible scheduling method of multiple mobile robots, which comprises the following steps: s1, establishing a mathematical model of a flexible scheduling method of multiple mobile robots, determining an objective function of multi-robot cooperative operation, then establishing a multi-workpiece, multi-procedure and multi-task scheduling model of the multiple mobile robots through the objective function, and determining that the minimum maximum completion time and the minimum total load of the multiple mobile robots are optimization targets; and S2, designing a genetic algorithm to solve the mathematical model established in the step S1, selecting an optimal population according to a solving result, decoding a gene chain of the population, reading the mobile robots corresponding to each procedure, and distributing all tasks to each mobile robot according to a time sequence for realizing intelligent scheduling of the multiple mobile robots. The invention solves the problem of multi-mobile robot scheduling with multiple tasks and multiple processes in a flexible workshop by using a genetic algorithm, effectively improves the production efficiency and saves the production cost.
Description
Technical Field
The invention belongs to the application field of multi-mobile robot cooperative work, and particularly relates to a flexible scheduling method of a multi-mobile robot.
Background
The industrial multi-mobile robot production line is a key supporting device for high-end manufacturing industries such as aerospace, maritime work equipment, rail transit, new energy automobiles, electronic manufacturing, electric power equipment and the like. In high-end manufacturing industry, large fixed multi-mobile robots with single functions are generally used, the multi-mobile robots lack mutual cooperation, once a manufacturing unit is fixed, workpieces which are similar to the production process of machining equipment can only be replaced, and the production efficiency of the multi-mobile robots is greatly limited. Meanwhile, due to the characteristics that more and more production and manufacturing are multi-task and multi-procedure, and small-batch products on a production line are changed into batch products of various varieties, the requirements on modularization, flexibility and intellectualization of manufactured processing units are higher and higher.
In an intelligent manufacturing plant, multiple mobile robots have long replaced workers, becoming a major productivity of high-end manufacturing. The multi-mobile robot is not only a flexible logistics transportation system, but also can realize actions such as grabbing, cutting and welding, drilling and riveting, polishing and grinding, high-precision measurement, assembly and detection and the like, and has a very high application prospect in an intelligent workshop. The flexible scheduling problem of the intelligent workshop is a difficult point. The problem can be divided into fully flexible scheduling and partially flexible scheduling: fully flexible scheduling means that all robots are universal, all processes can process all workpieces, and there is only a difference in processing efficiency between them, while partially flexible scheduling means that a single machine does not have the capability to complete all processes in a single workpiece. The fully flexible scheduling is only a special case of the partially flexible scheduling, and the partially flexible scheduling more conforms to the scheduling problem in real life. Therefore, flexible scheduling of multiple mobile robot parts becomes a critical problem to be solved urgently.
Disclosure of Invention
In view of the above, the invention provides a flexible scheduling method for multiple mobile robots, which solves the problem of efficient flexible scheduling among the multiple mobile robots, and realizes modularization, intellectualization and flexibility of processing units in an intelligent workshop.
In one aspect, the invention provides a flexible scheduling method for multiple mobile robots, which comprises the following steps:
s1, establishing a mathematical model of a flexible scheduling method of a multi-mobile robot, determining an objective function of multi-robot cooperative operation, then establishing a multi-workpiece, multi-procedure and multi-task scheduling model of the multi-mobile robot through the objective function, and determining that the minimum maximum completion time and the minimum total load of the multi-mobile robot are optimization targets;
and S2, designing a genetic algorithm to solve the mathematical model established in the step S1, selecting an optimal population according to a solving result, decoding a gene chain of the population, reading the mobile robots corresponding to each procedure, and distributing all tasks to each mobile robot according to a time sequence for realizing intelligent scheduling of the multiple mobile robots.
Further, in the mathematical model described in step S1, the benefit of a single robot to complete a task is expressed as:
Inc i ＝Re i -cos t Ri (1)
wherein i is a code of a mobile robot in the flexible processing unit, inc i For robot R i Profit from completion of the task, re i Is a robot R i COS t, the return of completing a task Ri Is a robot R i And (3) completing the task, wherein the aim of the multi-robot cooperative operating system is to realize the benefit maximization, namely the objective function is as follows:
wherein m refers to the number of mobile robots in the flexible processing unit.
Further, the mathematical model of step S1 further includes establishing a multi-workpiece scheduling model:
wherein f is 1 Representing the minimum value of the maximum completion time of a single workpiece in the flexible processing unit, j representing the code of the workpiece in the flexible processing unit, n representing the number of workpieces in the flexible processing unit, T j The completion time of the jth workpiece is shown, and the scheduling model of the multi-workpiece, multi-procedure and multi-task of the multi-mobile robot is
Wherein f is 2 Represents the minimum value of the total load of the multi-mobile robot, and g represents the flexibilityCoding of the processes in the processing unit, k representing the number of processes in the flexible processing unit, p ijg Represents the processing time of the g-th process of the j-th workpiece on the mobile robot i, q ijg The g-th process indicating whether the jth workpiece is processed on the mobile robot i is performed, and a value of 1 indicates yes, and a value of 0 indicates no.
Further, in the multi-mobile robot multi-workpiece, multi-process and multi-task scheduling model, the multi-mobile robot, the process and the workpiece are constrained by the following conditions:
at the same time, one workpiece can be processed on only one mobile robot;
at the same time, one mobile robot can only process one workpiece;
once the process begins to process, it cannot be interrupted until the process is completed;
the priority among different workpieces and the processes thereof is the same;
workpieces are not constrained with each other, but processes in the same workpiece are constrained before and after processing;
all robots can start processing at any time at the next moment as long as the robots are idle;
a single robot cannot process all the processes of a certain workpiece.
Further, in the multi-mobile robot multi-workpiece, multi-process and multi-task scheduling model, the multi-mobile robot is further constrained by the following conditions:
when the tasks of the multiple mobile robots are changed, the time for switching the end effectors by the multiple mobile robots is not considered;
when the multiple mobile robots execute tasks, faults which may occur to the multiple mobile robots are not considered;
the processing time for multiple mobile robots to accomplish various tasks is known.
Further, the genetic algorithm in step S2 specifically includes:
s21, coding the system by adopting a double-chain coding mode of a process chain and a machine chain, respectively initializing the machine chain and the process chain, finding out the minimum time required by processing all the processes in all the workpieces, the corresponding multi-mobile robots and the multi-mobile robots with the minimum time for processing a certain workpiece to complete the processes, and taking the multi-mobile robots as part of an initial population of a genetic algorithm;
s22, respectively performing cross operation on the machine chain and the process chain;
s23, performing mutation operation on the machine chain and the process chain respectively;
s24, selecting a part of elite populations according to the optimization target requirement;
s25, decoding and calculating a population fitness value function;
s26, judging whether the algorithm is terminated or not according to the termination condition, and if so, entering a step S27; otherwise, go back to step S22;
s27, finishing the algorithm, outputting an optimal solution, and selecting an optimal population;
and S28, decoding the gene chain of the population according to the selected optimal population, reading the mobile robots corresponding to the processes, and distributing all tasks to the mobile robots according to time sequence for realizing intelligent scheduling of the multiple mobile robots.
Further, in step S21, a machine chain and a process chain are initialized by a method in which a stochastic method and a greedy method are mixed;
and/or, in step S22, the machine chain performs the cross operation by using a method of mixing a partial mapping method with an ordered cross method, and the process chain performs the cross operation by using a priority operation cross method;
and/or, in step S24, selecting an elite population by means of a roulette and elite retention strategy.
Further, in step S23, the machine chain is mutated by the single-point mutation method, and the process chain is mutated by the reverse-order mutation method.
Further, in step S23, an adaptive mutation probability is designed, that is, a number is randomly generated, and when the number is greater than the following mutation probability, the population performs a mutation operation, and the mutation probability calculation formula is:
wherein the content of the first and second substances,
is the variation probability of the population of the kth generation,
f represents the maximum value and the average value of the fitness of the population of the kth generation and the fitness value of the individual respectively, m 1 、m 2 Is a constant greater than zero and m 1 <m 2 。
Further, in step S26, the termination condition is that the maximum number of iterations is reached and/or the maximum fitness change number of the population reaches a set value.
The flexible scheduling method of the multi-mobile robot provided by the invention determines a target function by establishing a mathematical model of the flexible scheduling method of the multi-mobile robot, designs a genetic algorithm double-layer coding structure, and obtains a feasible solution by utilizing crossing, variation, selection and assistance of an elite reservation strategy, thereby realizing multi-task allocation of the multi-mobile robot in a flexible workshop.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate an embodiment of the invention and, together with the description, serve to explain the invention and not to limit the invention. In the drawings:
fig. 1 is a flowchart of a flexible scheduling method for a multi-mobile robot according to an embodiment of the present invention;
FIG. 2 is a diagram of a two-level encoding scheme for the machine chain and process chain of the present invention;
FIG. 3 is a Partial Mapping (PMX) diagram of the present invention;
FIG. 4 is a diagram of an ordered cross-hatch method of the present invention;
fig. 5 is a priority operation crossover (POX) diagram of the present invention.
Detailed Description
It should be noted that the embodiments and features of the embodiments may be combined with each other without conflict. The present invention will be described in detail below with reference to the embodiments with reference to the attached drawings.
As shown in fig. 1, the present invention provides a flexible scheduling method for multiple mobile robots, which specifically includes the following steps:
s1, establishing a mathematical model of a flexible scheduling method of the multi-mobile robot, determining an objective function of multi-robot cooperative operation, then establishing a multi-workpiece, multi-procedure and multi-task scheduling model of the multi-mobile robot through the objective function, and determining that the minimum maximum completion time and the minimum total load of the multi-mobile robot are optimization targets.
S2, designing a genetic algorithm to solve the mathematical model established in the step S1, selecting an optimal population according to a solving result, decoding a gene chain of the population, reading the mobile robots corresponding to each procedure, and distributing all tasks to each mobile robot according to a time sequence, wherein the intelligent scheduling for realizing the multi-mobile robot specifically comprises the following steps:
s21, coding the system by adopting a double-chain coding mode of a process chain and a machine chain, respectively initializing the machine chain and the process chain, finding out the minimum time required by processing all the processes in all the workpieces and the corresponding multi-mobile robots and the multi-mobile robots with the minimum time for processing a certain workpiece to complete the processes, and taking the multi-mobile robots as part of an initial population of a genetic algorithm.
Fig. 2 is a double-layer coding structure diagram of the machine chain and the process chain of the present invention, as shown in fig. 2, numbers in the process chain and the gene chain respectively represent a workpiece and a mobile machine number, the number of times that the same number appears from left to right in the process chain represents the work number of the workpiece, and if T31 represents the first process of the workpiece 3, the process chain in fig. 2 represents that [ T31T 11T 12T 21T 32T 22T 33] respectively corresponds to the mobile robot number [ 31 24 3 4].
And S22, respectively performing cross operation on the machine chain and the process chain. The crossover operator is the core of the genetic algorithm and mainly has the main functions of generating new next generation individuals after the crossover of every two chromosomes and expanding the diversity of the gene library.
Preferably, in step S22, the machine chain performs the interleaving operation by using a method in which a partial mapping method (PMX) is mixed with an ordered interleaving method (OX). As shown in FIG. 3, the partial mapping method is to arbitrarily select two individuals, choose two gene positions in two chromosomes, reserve the genes in the middle of the two gene positions to be inherited by offspring, and exchange other gene positions of the two chromosomes. As shown in fig. 4, the ordered crossover method is to randomly select two children and optionally two workpiece numbers, and interchange the machine numbers of the parents corresponding to the workpiece numbers to form new offspring chromosomes, as shown in fig. 4, select any workpiece in the two individuals, that is, select workpiece 3, whose corresponding machine numbers are [3,3,4] and [4,1,3], respectively, and exchange the two genes while keeping the other genes unchanged, so as to form two new chromosomes and complete one generation crossover. The ordered crossover operator can effectively inherit part of genes of parents to form, so that the genetic function is achieved, blind search is not carried out, the ordered crossover operator tends to enable a group to have more excellent genes, and the purpose of optimizing is finally achieved.
Preferably, in step S22, the process chain performs the crossover operation in a priority operation crossover (POX) mode. The method is characterized in that all workpieces are randomly divided into two non-empty subsets A and B, optionally two individuals are parents, workpiece processes in the parent subset A are reserved, and workpiece processes in the subset B are interchanged to form a next generation chromosome. As shown in fig. 4, dividing the workpiece set a = [1,2], B = [3,4], selecting two individual parents, transmitting the workpiece process genes in the set a to the offspring, and exchanging the workpiece process gene positions in the set B, as specifically shown in fig. 5.
The crossover probability of the crossover operator in the genetic algorithm is generally set as a fixed value, the crossover probability of each generation is the same, and the self-adaptive crossover probability is set up in the text, namely the crossover probability is higher in the early stage of evolution, the population can be quickly optimized, and the crossover probability is lower in the later stage. The expression for the cross probability is:
in the formula (I), the compound is shown in the specification,
the k-th generation and the initial cross probability, respectively, and σ is a fixed value representing the convergence speed of the function.
And S23, performing mutation operation on the machine chain and the process chain respectively.
Preferably, in step S23, the machine chain performs mutation operation by using a single point mutation method, that is, randomly selecting two gene sites on a mutated chromosome, and then reversing the sequence of the gene between the two gene sites; the process chain adopts a reverse order mutation mode to perform mutation operation, namely a gene position is randomly selected in a mutation chromosome, and a machine number is randomly selected in a process selectable machine corresponding to the gene position to replace the gene position.
Designing adaptive mutation probability, that is, randomly generating a number, and performing mutation operation on the population when the number is greater than the following mutation probability
In the formula (I), the compound is shown in the specification,
is the variation probability of the population of the kth generation,
f represents the maximum value and the average value of the fitness of the population of the kth generation and the fitness value m of the individual respectively 1 、m 2 Is a constant greater than zero and m 1 ＜m 2 。
And S24, selecting a part of elite populations according to the optimization target requirements.
Preferably, the elite population is selected by the method of roulette and elite retention strategy.
And S25, decoding and calculating a population fitness value function.
Preferably, the invention adopts an interpolation sequence decoding method, a gene is read from a process chain and a machine chain from left to right in sequence, the number of times of occurrence of the gene on the process chain is calculated and recorded as the work number of the workpiece, if the task is the first task of the selected machine, the processing is started after the end time of the last process of the task, if the task is the first process of the same workpiece, the processing is performed from the zero time of the corresponding machine, otherwise, all idle time periods in the machine are searched and marked as (T _ Down, T _ UP), the completion time of the last process of the process and the workpiece and the size of T _ Down are judged, TM represents the maximum value of the two, whether T _ UP-TM is more than or equal to the required processing time T of the process on the selected machine is judged, if a certain time interval T _ UP-TM > = T, the task can be completed in advance to the idle time, the start time is TM, otherwise, the process is arranged to the last side of the selected machine timing sequence.
S26, judging whether the algorithm is terminated or not according to the termination condition, and if so, entering a step S27; otherwise, a jump is made back to step S22. Preferably, the termination condition is that a maximum number of iterations is reached and/or a maximum fitness change number of the population reaches a set value.
And S27, finishing the algorithm, outputting an optimal solution, and selecting an optimal population. The correspondence between the selected optimal population gene chains is the correspondence between the mobile robot and the workpiece process.
And S28, decoding the gene chain of the population according to the selected optimal population, reading the mobile robots corresponding to the working procedures, and distributing all tasks to the mobile robots according to the time sequence for realizing the intelligent scheduling of the multiple mobile robots.
In a further technical solution, aiming at step S1, a multi-mobile robot profit model is first designed: in the case of m machines R = { R = 1 ,R 2 ...R m In the flexible processing unit formed by the robots, the robots have the capability of completing corresponding tasks, such as welding, grinding, polishing and the like, by using the vector B ri Indicates whether there is such capability, B ri ＝{b r1 ,b r2 ,...b rk }，b ri Is 0 or 1, when b ri =0 indicates that the robot does not have the ability to complete the task; b ri =1 indicates that the robot has the capability to complete the task. Robot R i The cost of completing the task is cos t Ri ＝{cos t i1 ,cos t i2 ,...cos t ik It represents the cost of completing the task, which may be time, energy consumption, etc., and the time cost is mainly considered herein. Definition of robot R i The return for completing a certain task is Re, re = { Re = { (Re) 1 ,re 2 ,...re k }. The yield of the robot is expressed by Inc, then the robot R i The benefit of completing a task can be expressed as:
Inc i ＝Re i -cos t Ri (1)
wherein i is a code of a mobile robot in the flexible processing unit, inc i For robot R i Profit from completion of the task, re i Is a robot R i COS t, the return of completing a task Ri Is a robot R i And (3) completing the task, wherein the goal of the multi-robot cooperative operating system is to realize the maximum benefit, namely the objective function is as follows:
wherein m refers to the number of mobile robots in the flexible processing unit.
Meanwhile, aiming at the step S1, a multi-workpiece, multi-procedure and multi-task scheduling model of the multi-mobile robot is established: in the system, n workpieces { T } need to be processed 1 ，T 2 ,...T n And each workpiece has one or more processes, and in the same workpiece, the processing sequence of the processes is determined and the processes can be processed on different machines, but the processing time of the same process is different for different machines. The initial positions of the mobile robots are different, the positions of the workpieces are also different, and the flexible scheduling of the multiple mobile robots aims to distribute a processing task to each robot and determine the processing of the task on each machineAnd in consideration of the fact that the remuneration obtained by the mobile robots for completing the same work piece in the same process is equal, the moving speeds of the multiple mobile robots are the same, the machining positions of the workpieces are fixed, and the completion time of each workpiece most comprises the moving time Tm and the machining duration Tl of the robots. Therefore, the operators (1) and (2) can be converted into the problem of solving the minimum maximum completion time and the minimum total load of the machine:
wherein, f 1 Representing the minimum value of the maximum completion time of a single workpiece in the flexible processing unit, j representing the code of the workpiece in the flexible processing unit, n representing the number of workpieces in the flexible processing unit, T j Indicating the completion time of the jth workpiece.
Wherein f is 2 Representing the minimum value of the total load of the multi-mobile robot, g representing the code of the process in the flexible processing unit, k representing the number of processes in the flexible processing unit, p ijg Represents the processing time of the g-th process of the j-th workpiece on the mobile robot i, q ijg The g-th process indicating whether the jth workpiece is processed on the mobile robot i is performed, and a value of 1 indicates yes, and a value of 0 indicates no.
It should be noted that the above method can use a weight method to realize multi-objective optimization, that is:
in the formula w i The weight value can be determined according to the importance of the optimization target.
In addition, in the above model, it should be noted that there are certain constraints and assumptions between the multi-mobile robot and the workpiece:
(1) At the same time, one workpiece can only be processed on one machine;
(2) At the same time, one machine can only process one workpiece;
(3) Once a process begins to process, it cannot be interrupted until the process is finished;
(4) The priority among different workpieces and the processes thereof is the same;
(5) Workpieces are not constrained with each other, but processes in the same workpiece are constrained before and after processing;
(6) All robots can start processing at any time at the next moment as long as the robots are idle;
(7) A single robot cannot process all the processes of a certain workpiece.
Further, in the multi-mobile robot multi-workpiece, multi-process and multi-task scheduling model, the multi-mobile robot is further constrained by the following conditions:
(1) When the tasks of the multiple mobile robots are changed, the time for switching the end effectors by the multiple mobile robots is not considered;
(2) When the multiple mobile robots execute tasks, faults which may occur to the multiple mobile robots are not considered;
(3) The processing time for multiple mobile robots to accomplish various tasks is known.
It should be noted that, in step S21 of the present invention, the formed initial population is adjusted, and for part of the flexible scheduling problems, if the mobile robot may not be able to complete all tasks, the adjustment is needed, and the adjustment strategy is as follows: if it can complete the process T ij Has a robot number set of N ij The number of the machines in the set is Mn, the total number of the robots is M, and the procedure T ij Corresponding machine chain gene position n, if n is less than or equal to Mn, then tableSelection set N ij The nth element number in the first element number is used as a processing machine; if N is greater than Mn, it represents the selection set N ij The kth element inside as the processing machine, k = mod (n, mn) +1; where mod (n, mn) represents the remainder of n divided by Mn.
In summary, the flexible scheduling method for multiple mobile robots provided by the invention determines the objective function by establishing a mathematical model of the flexible scheduling method for multiple mobile robots, designs a genetic algorithm double-layer coding structure, and obtains a feasible solution by using a crossover, variation, selection and assistance with an elite reservation strategy, thereby realizing multi-task allocation of multiple mobile robots in a flexible workshop.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.
Claims (9)
1. A flexible scheduling method of multiple mobile robots is characterized by comprising the following steps:
s1, establishing a mathematical model of a flexible scheduling method of a multi-mobile robot, determining an objective function of multi-robot cooperative operation, then establishing a multi-workpiece, multi-procedure and multi-task scheduling model of the multi-mobile robot through the objective function, and determining that the minimum maximum completion time and the minimum total load of the multi-mobile robot are optimization targets;
s2, designing a genetic algorithm to solve the mathematical model established in the step S1, selecting an optimal population according to a solving result, decoding a gene chain of the population, reading the mobile robots corresponding to each procedure, and distributing all tasks to each mobile robot according to a time sequence for realizing intelligent scheduling of the multiple mobile robots, wherein the genetic algorithm specifically comprises the following steps:
s21, coding the system by adopting a double-chain coding mode of a process chain and a machine chain, respectively initializing the machine chain and the process chain, finding out the minimum time required by processing all the processes in all the workpieces, the corresponding multi-mobile robots and the multi-mobile robots which can finish the processes for processing one workpiece, and taking the minimum time as a partial initial population of a genetic algorithm;
s22, respectively performing cross operation on the machine chain and the process chain;
s23, performing mutation operation on the machine chain and the process chain respectively;
s24, selecting a part of elite populations according to the optimization target requirements;
s25, decoding and calculating a population fitness value function;
s26, judging whether the algorithm is terminated or not according to the termination condition, and if so, entering a step S27; otherwise, go back to step S22;
s27, finishing the algorithm, outputting an optimal solution, and selecting an optimal population;
and S28, decoding the gene chain of the population according to the selected optimal population, reading the mobile robots corresponding to the working procedures, and distributing all tasks to the mobile robots according to the time sequence for realizing the intelligent scheduling of the multiple mobile robots.
2. The flexible scheduling method of claim 1, wherein in the mathematical model of step S1, the yield of a single robot to complete a task is represented as:
Inc i ＝Re i -cost Ri (1)
wherein i is a code of a mobile robot in the flexible processing unit, inc i Is a robot R i Profit from completion of the task, re i Is a robot R i Cost, the reward for completing a task Ri Is a robot R i And (3) completing the task, wherein the goal of the multi-robot cooperative operating system is to realize the maximum benefit, namely the objective function is as follows:
wherein m refers to the number of mobile robots in the flexible processing unit.
3. The flexible scheduling method of multi-mobile robot of claim 2, wherein the mathematical model of step S1 further comprises establishing a multi-workpiece scheduling model:
wherein f is 1 Representing the minimum value of the maximum completion time of a single workpiece in the flexible processing unit, j representing the code of the workpiece in the flexible processing unit, n representing the number of workpieces in the flexible processing unit, T j The completion time of the jth workpiece is shown, and the scheduling model of the multi-workpiece, multi-procedure and multi-task of the multi-mobile robot is
Wherein f is 2 Representing the minimum value of the total load of the multi-mobile robot, g representing the code of the process in the flexible processing unit, k representing the number of processes in the flexible processing unit, p ijg Represents the processing time of the g-th process of the j-th workpiece on the mobile robot i, q ijg The g-th process indicating whether the jth workpiece is processed on the mobile robot i is performed, and a value of 1 indicates yes, and a value of 0 indicates no.
4. The flexible scheduling method of multi-mobile robot according to claim 3, wherein in the multi-mobile robot multi-workpiece, multi-process, multi-task scheduling model, the multi-mobile robot and the process, workpiece are constrained by the following conditions:
at the same time, one workpiece can be processed on only one mobile robot;
at the same time, one mobile robot can only process one workpiece;
once the process begins to process, it cannot be interrupted until the process is completed;
the priority among different workpieces and the processes thereof is the same;
workpieces are not constrained with each other, but processes in the same workpiece are constrained before and after processing;
all robots can start processing at any time at the next moment as long as the robots are idle;
a single robot cannot process all the processes of a certain workpiece.
5. The flexible scheduling method of claim 3 wherein in the multi-mobile robot multi-workpiece, multi-process, multi-task scheduling model, the multi-mobile robot is further constrained by the following conditions:
when the tasks of the multiple mobile robots are changed, the time for switching the end effectors by the multiple mobile robots is not considered;
when the multiple mobile robots execute tasks, faults which may occur to the multiple mobile robots are not considered;
the processing time for multiple mobile robots to accomplish various tasks is known.
6. The flexible scheduling method of a multi-mobile robot according to claim 1, wherein:
in step S21, initializing a machine chain and a process chain by a method in which a random method and a greedy method are mixed;
and/or, in step S22, the machine chain performs the cross operation by using a method of mixing a partial mapping method with an ordered cross method, and the process chain performs the cross operation by using a priority operation cross method;
and/or, in step S24, selecting an elite population by means of a roulette and elite retention strategy.
7. The flexible scheduling method of claim 1 wherein in step S23, the machine chain is mutated by a single point mutation method, and the process chain is mutated by a reverse order mutation method.
8. The flexible scheduling method of claim 7, wherein in step S23, an adaptive mutation probability is designed, that is, a number is randomly generated, and when the number is greater than the following mutation probability, the population is mutated, and the mutation probability is calculated according to the following formula:
wherein the content of the first and second substances,
is the variation probability of the population of the kth generation,
f represents the maximum value and the average value of the population fitness of the kth generation and the fitness value of an individual respectively, m 1 、m 2 Is a constant greater than zero and m 1 ＜m 2 。
9. The flexible scheduling method of claim 1, wherein in step S26, the termination condition is that a maximum number of iterations is reached and/or a maximum fitness change number of the population reaches a set value.
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